• Energy Research

Selective removal of coatings by lasers can facilitate the reuse of coated tools in a circular economy. In order to optimise and control the process, it is essential to study the impact of process input variables on process performance. In this paper, coating removal from tooling was carried out using a picosecond a pulsed fibre laser, in order to investigate the effects of laser pulse energy, pulse frequency, galvo scanning speed and scanning track stepover. A fractional factorial design of experiments and analysis of variance was used to optimise the process; considering cleaning rate, specific energy consumption and surface integrity as assessed by changes in surface roughness and composition of the tooling after laser cleaning. The results shows synergy between cleaning rate and specific energy with the laser pulse frequency and galvo scanning speed as the two most significant factors, while the laser pulse energy had the greatest contribution to changes in surface composition. Based on extensive experiments, the relationship between processing rate and system specific energy consumption was mathematically modelled. The paper contributes a new specific energy model for laser cleaning and provides a benchmark of the process energy requirements compared to other manufacturing processes. Additionally, the generic scientific learning from this is that the rate of energy input is a key tool for maximising cleaning rate and minimising specific energy requirements, while the intensity of energy applied, is a key metric that influences surface integrity. More complex factors, influence the surface integrity.

This study presents an approach for determining energy efficient toolpaths using numerical control (NC)-based energy demand software. To achieve this, NC programmes were generated for the true spiral, rectangular spiral, and square contour toolpaths from HyperMill, a commercially available Computer-Aided Manufacturing (CAM) software for performing pocket milling of AISI 1018 steel on a 3-axis CNC milling machine. These programmes were uploaded as input on the graphical user interface (GUI) of the NC code-based energy demand software. The result obtained from NC code-based energy software was validated against the theoretical total energy and processing time, and the pocket milling of AISI 1018 steel on a 3-axis CNC milling machine. The theoretical, software, and experimental analyses show that the true spiral toolpath had the lowest total electrical energy demand and processing time. The result also shows that the energy demand software could be adopted to accurately predict the total electrical energy and processing time pre-machining. This could save setting up and trial by error practices and costs. Further studies included surface roughness analyses of the machined pockets after milling, and an improved surface finish of the pocket was obtained with the true spiral toolpath when compared with the other considered toolpaths. Therefore, for energy efficient machining, it is recommended that NC code-based energy demand software which incorporates the weights of feed axes, vice, and workpiece, as well as the power required by the feed drive during cutting should be adopted for the accurate prediction of total electrical energy demand and total processing time of a machining process.

Abstract In the past, the cutting conditions that meet the economic and environmental objectives of the specified manufacturing process were selected based on minimum tooling cost and/or minimum electrical energy criterion. However, detailed modelling of electrical energy based on tool life and cost criterion has not been addressed. In this study, machining tests were conducted to develop a cost model which includes machining energy, and to assess the impact of the extended tool life model with regards to selection of cutting conditions, electricity cost and tool wear effect that satisfy these objectives. The model was validated with an industrial case study. Results show that cost savings at minimum energy were achieved. Hence, substantial cost savings could be achieved by selecting optimized machining parameters which could reduce machining costs by 47% compared to using tool supplier recommended feeds, depth of cut and cutting velocity. Thus, cost could be optimized fairly accurately without explicitly modelling energy demand due to the relative low contribution of energy costs compared to tooling costs. The optimized energy costs leads to minimum associated carbon footprint and reduces overall product cost. This creates an incentive for manufacturing companies to investigate the sustainability and energy efficiency of their manufacturing processes.

Abstract Machine tool axis motions are the key movements when milling components and hence it is vital to understand how they influence energy demand in manufacturing. In this research, a systematic study of component and toolpath orientation was undertaken for milling operations. The current and voltage demand was monitored and this allowed evaluation of electrical energy demand. The machine tool was run while executing toolpaths in air (air cutting) and then in actual pocket milling of AISI 1018 steel and the component was rotated in the x-y plane of the machine. It was shown that when machining toolpaths were aligned to the lighter axis, this reduced the feed electrical energy demand by 29%, minimised the drive dynamics, and reduced surface roughness by up to 50%. Different toolpaths were tested in machining a pocket. The most energy efficient toolpath strategy had the best surface finish. Thus there are synergies in setting and programming toolpaths allowing simultaneous reduction of energy demand and component surface roughness. The knowledge obtained in this study is vital guidance for process planners.

Laser cleaning technology is a promising technology for selective removal of coatings from substrates to facilitate re-use and recoating. A leading coating of interest for reduced friction and operating forces is Diamond like Carbon (DLC) coating. This paper reports on the energy requirements and associated unit process scope 2 emissions for the DLC laser removal process to facilitate re-use of tooling in a circular economy. This work can assist in providing a greater understanding of the energy requirements and of energy consumption and emissions reduction strategies for the DLC removal process. This is important for the transition to net-zero emissions in manufacturing.

Abstract In a circular economy, resources are kept in use for as long as possible, extracting the maximum value from them whilst in use, then recovering and regenerating products and materials at the end of each service life. To enable a transition to a circular economy it is important to establish the factors that would trigger and sustain such an economy and the extent to which aspects of the circular economy are already embedded in countries. This research focused on a comparative analysis of the United Kingdom and South Africa composite manufacturers in relation to circular economy for composites materials. Key considerations such as the drivers, sustainers, barriers, ownership models, volume of composite waste from production operations, and current recycling or disposal practices were studied. For both countries, the opportunities to reduce cost were found to be a very strong and a common driver and sustainer for re-use and recycling of composite waste from manufacturing operations. The range of findings helps in understanding the national context and international synergies in transition to circular economy for composite materials.

Laser welding, a key and competitive technology for advanced industrial manufacturing is powered by energy. With increasing attention to energy management and environmental protection in manufacturing, reducing energy consumption is imperative. However, the traditional optimization of laser welding has been mainly focused on welding quality without considering the total energy consumption of all components. Thus, this paper researches the relationship between welding quality and energy consumption by optimizing process parameters during the 2 mm thickness 6061 aluminum alloy laser welding. Firstly, a three-factor, three-level experiment using Central Composite Design (CCD) is constructed. Secondly, Kriging models are employed to construct the models between process parameters and objectives. Finally, the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) is utilized to minimize energy consumption while maintaining good welding quality. The study shows that compared to the initial scenario, optimization can reduce energy consumption while increasing welding quality.

Abstract Manufacturing industries have objectives of improving productivity and economic growth while simultaneously reducing energy consumption and lessening environmental impact. Energy is a vital resource for the manufacturing industry. However, environmental impacts are a major concern for energy use in manufacturing. It is important to control and manage the way energy is consumed to reduce costs and carbon emissions. This research focused on the energy consumption and carbon emissions for the manufacturing industry in South Africa from 1999 to 2015. Index decomposition analysis (IDA) and historical trends analysis were used to analyse drivers of manufacturing energy consumption and to understand rebound effects respectively. Sub-sectoral analysis alongside the index of energy effectiveness (IoEE) was formulated which analysed and ranked selected manufacturing sub-sectors in terms of its effectiveness in energy use. Index decomposition analysis (IDA) has shown that the reductions in energy consumption within manufacturing was predominantly driven by reductions in energy intensities for the period of interest. Further analysis of changes in energy effectiveness against production changes also support the evidence that production output increases has not wiped out energy effectiveness gains, displaying little rebound effect. Pareto and sub-sectoral contribution analysis revealed that (i) the manufacture of basic iron and steel and (ii) the manufacture of refined petroleum products, chemicals and chemical products contribute 80% of the aggregate energy consumption and are amongst the least effective in energy use. The combined analysis in this study is aimed at policymakers, government and industries to inform energy reduction strategies.